Pipeline for Observational Data Processing Analysis and CollaborationView the Source — Explore Jupyter Notebooks
PODPAC is a python library that builds on the scientific python ecosystem to enable simple, reproducible geospatial analyses that run locally or in the cloud.
import podpac # elevation elevation = podpac.data.Rasterio(source="elevation.tif") # soil moisture soil_moisture = podpac.data.H5PY(source="smap.h5", interpolation="bilinear") # evaluate soil moisture at the coordinates of the elevation data output = soil_moisture.eval(elevation.coordinates) # run evaluation in the cloud aws_node = podpac.managers.aws.Lambda(source=soil_moisture) output = aws_node.eval(elevation.coordinates)
Data wrangling and processing of geospatial data should be seamless so that earth scientists can focus on science. The purpose of PODPAC is to facilitate:
Access of data products
Subsetting of data products
Projecting and interpolating data products
Combining/compositing data products
Analysis of data products
Sharing of algorithms and data products
Use of cloud computing architectures (AWS) for processing
This material is based upon work supported by NASA under Contract No 80NSSC18C0061.